Causal Bandits: Learning Good Interventions via Causal Inference
Authors: Finnian Lattimore, Tor Lattimore, Mark D. Reid
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments We compare Algorithms 1 and 2 with the Successive Reject algorithm of Audibert and Bubeck (2010), Thompson Sampling and UCB under a variety of conditions. ... For each experiment, we show the average regret over 10,000 simulations with error bars displaying three standard errors. |
| Researcher Affiliation | Collaboration | Finnian Lattimore Australian National University and Data61/NICTA finn.lattimore@gmail.com Tor Lattimore Indiana University, Bloomington tor.lattimore@gmail.com Mark D. Reid Australian National University and Data61/NICTA mark.reid@anu.edu.au |
| Pseudocode | Yes | Algorithm 1 Parallel Bandit Algorithm |
| Open Source Code | Yes | The code is available from <https://github.com/finnhacks42/causal_bandits> |
| Open Datasets | No | No concrete access information (specific link, DOI, repository name, formal citation with authors/year) for a publicly available or open dataset was found. The paper describes using a synthetic model for its experiments: 'Throughout we use a model in which Y depends only on a single variable X1 (this is unknown to the algorithms). Yt Bernoulli( 1 2 + ε) if X1 = 1 and Yt Bernoulli( 1 2 ε ) otherwise, where ε = q1ε/(1 q1).' |
| Dataset Splits | No | No specific dataset split information (percentages, sample counts, citations to predefined splits) was provided. The paper describes a sequential decision problem where data is collected iteratively over 'T rounds' rather than using pre-defined dataset splits. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running experiments were mentioned. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) were mentioned. |
| Experiment Setup | Yes | For the first T/2 rounds it chooses do() to collect observational data. ... In Figure 2a we fix the number of variables N and the horizon T and compare the performance of the algorithms as m increases. ... Throughout we use a model in which Y depends only on a single variable X1 (this is unknown to the algorithms). Yt Bernoulli( 1 2 + ε) if X1 = 1 and Yt Bernoulli( 1 2 ε ) otherwise, where ε = q1ε/(1 q1). ... Input: Total rounds T and N. (Algorithm 1) ... Input: T, η [0, 1]A, B [0, )A (Algorithm 2) |